Sistem Pendukung Keputusan Peringatan Dini Kualitas Udara Yogyakarta Berbasis LSTM, SHAP, dan Fuzzy Tsukamoto

The escalating urban air pollution in Yogyakarta City, particularly fine particulate matter (PM2.5), demands an air quality monitoring system that is not merely reactive, but predictive and capable of guiding public policy mitigation. This research proposes an early warning Decision Support System (DSS) for air quality by integrating Multi-Output Long Short-Term Memory (LSTM), Explainable Artificial Intelligence (XAI) using SHapley Additive exPlanations (SHAP), and Tsukamoto Fuzzy Logic. The Multi-Output LSTM network simultaneously forecasts six Air Pollutant Standard Index (ISPU) parameters based on historical sequences from the Air Quality Monitoring System (AQMS) station during 2021–2024. The SHAP method dismantles the black-box nature of the neural network to uncover atmospheric causality dynamics, while the Tsukamoto Fuzzy inference engine transforms raw numerical predictions into hazard alert levels and structured public health recommendations. Forecasting evaluation demonstrates high model accuracy, achieving Root Mean Square Error (RMSE) metrics of 4.82 for SO2, 1.86 for CO, 6.48 for PM10, 9.50 for PM2.5, 7.33 for O3, and 11.55 for NO2. SHAP feature attribution validated via statistical cross-correlation analysis confirms that PM2.5 concentration surges are significantly driven by precursor NO2 emissions with a 1-to-2-day lag (r = 0.2701, p < 0.001) through secondary photochemical reactions. DSS testing across six extreme weather scenarios proves the adaptive capability of the system in delivering proportional alerts. However, the system exhibits limitations including reliance on a single monitoring station and stochastic meteorological uncertainty requiring careful policy calibration.